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FMRG: Artificial Intelligence Driven Cybermanufacturing of Quantum Material Architectures

FMRG: Artificial Intelligence Driven Cybermanufacturing of Quantum Material Architectures
FMRG:人工智能驱动的量子材料架构网络制造
批准号:
2036359
负责人:
Radhika Nagpal
金额:
$375.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-09-01 至 2022-09-30

项目摘要

项目成果

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中文摘要
翻译
量子材料结构由石墨烯和其他二维材料组成,这些材料在精确的三维结构中堆叠时,表现出独特的可调机械、电气、光学和磁性能。这些三维结构具有广泛的潜在应用,是微芯片、电池、天线、化学和生物传感器、太阳能电池和神经接口的极有前途的组件。然而,目前,由于缺乏对物理和化学过程的基本理解,很难控制或规模化制造这些三维结构。这项未来制造(FM)拨款旨在使用网络制造方法为量子材料架构开发一个变革性的未来制造平台,该方法将人工智能、机器人技术、多尺度建模和预测模拟相结合,用于将多种二维材料自动化和平行组装成复杂的三维结构。该平台使未来能够为广泛和关键应用生产高质量的定制量子材料架构,支持美国在技术开发方面的持续领导地位。网络制造的研究与创新的教育计划相结合,为科学家和工程师提供跨学科培训,特别是在先进制造,人工智能和量子结构方面,特别是女性和代表性不足的少数民族,以及让公众参与未来的制造概念。这项资助研究的重点是使用微流控组件可扩展制造3D量子材料结构或范德华异质结构(vdWHs)的一种全新方法。vdWHs由原子薄层的无限组合组成,并显示出有趣的新兴功能。关键的工艺创新是二维材料的精密微流体折叠,这已经在小规模上得到了证明。这种方法具有扩大到晶圆规模的潜力,在规模上没有基本限制。第二个关键创新是在制造流程的各个方面嵌入人工智能(AI),从低级精度控制到自动化表征,再到高级结构预测。预测模拟和可视化工具与原位光谱相结合,可以实时分析原子尺度的物理和化学过程及其控制。此外,微流体环境中的平行自组装被研究为实现真正可扩展制造的途径。该奖项的预期结果是生产由数十个原子层组成的超晶格,这些原子层具有精确设计的堆叠顺序和排列方式,从而产生具有传统材料结构无法获得的电子和光子特性的全新定制量子材料结构。这项研究推进了材料物理学、纳米电子学和光子科学的基础知识,为制造未来的设备(如涡旋电子学)铺平了道路。一个关键的成果是人工智能驱动,机器人控制的网络制造微流控平台,能够为新兴的量子和其他设备应用制造复杂的结构。这项未来制造研究补助金由工程局以下部门提供支持:土木、机械和制造创新;电气、通信和网络系统;工程教育和中心;数学和物理科学的以下部门:材料研究;化学;和数学科学。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Quantum material architectures consist of graphene and other two-dimensional materials, which, when stacked in precise three-dimensional architectures, exhibit unique and tunable mechanical, electrical, optical, and magnetic properties. These three-dimensional architectures have broad potential applications and are highly promising components for microchips, batteries, antennas, chemical and biological sensors, solar-cells and neural interfaces. However, currently, due to the lack of fundamental understanding of the physical and chemical processes, it has been difficult to control or scale the manufacturing of these three-dimensional structures. This Future Manufacturing (FM) grant is to develop a transformative Future Manufacturing platform for quantum material architectures using a cybermanufacturing approach, which combines artificial intelligence, robotics, multiscale modeling, and predictive simulation for the automated and parallel assembly of multiple two-dimensional materials into complex three-dimensional structures. This platform enables future production of high-quality, custom quantum material architectures for broad and critical applications, supporting continued U.S. leadership in technology development. The research in cybermanufacturing is integrated with innovative educational programs for cross-disciplinary training of scientists and engineers, especially, women and underrepresented minorities, in advanced manufacturing, artificial intelligence and quantum structures, as well as engaging the public in future manufacturing concepts. This grant research focuses on a fundamentally new method for scalable manufacturing of 3D quantum material architectures or van der Waals heterostructures (vdWHs) using microfluidic assembly. vdWHs are composed of unlimited combinations of atomically thin layers and exhibit interesting emerging functionalities. The key process innovation is precision microfluidic folding of 2D materials, which has been demonstrated at a small-scale. This method has promising potential to scale up to wafer scale, with no fundamental limit on scaling. A second key innovation is embedding artificial intelligence (AI) across all aspects of the manufacturing process flow, from low-level precision control, to automated characterization, to high-level structure predictions. Predictive simulation and visualization tools combined with in situ spectroscopy allow real-time analysis of atomic-scale physical and chemical processes and their control. Moreover, parallel self-assembly in microfluidic environments is investigated as a pathway toward truly scalable manufacturing. The expected outcome of the award is to produce superlattices consisting of tens of atomic layers with precisely engineered stacking order and alignment, leading to fundamentally new custom quantum material architectures with electronic and photonic properties impossible to obtain from conventional material architectures. This research advances fundamental knowledge in material physics, nanoscale electronics and photonic science leading the way to manufacturing of future devices, such as twistronics. A key outcome is an AI-driven, robotics-controlled cybermanufacturing microfluidic platform that is capable of manufacturing complex structures for emerging quantum and other device applications.This Future Manufacturing research grant is supported by the following Divisions in the Engineering Directorate: Civil, Mechanical and Manufacturing Innovation; Electrical, Communications and Cyber Systems; and Engineering Education and Centers; and the following Divisions in the Mathematical and Physical Sciences: Materials Research; Chemistry; and Mathematical Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
Thickness dependence of dielectric constant of alumina films based on first-principles calculations
基于第一性原理计算的氧化铝薄膜介电常数的厚度依赖性
DOI: 10.1063/5.0106721
发表时间: 2022
期刊: Applied Physics Letters
影响因子: 4
作者: [Fukushima, Shogo, Kalia, Rajiv K., Nakano, Aiichiro, Shimojo, Fuyuki, Vashishta, Priya]
通讯作者: Vashishta, Priya
DOI: 10.1109/jiot.2022.3170449
发表时间: 2022-08
期刊: IEEE Internet of Things Journal
影响因子: 10.6
作者: [Babajide A. Salau;A. Rawal;D. Rawat]
通讯作者: Babajide A. Salau;A. Rawal;D. Rawat
DOI: 10.1063/5.0031806
发表时间: 2021-02
期刊: Applied Physics Letters
影响因子: 4
作者: [P. Rajak;A. Nakano;P. Vashishta;R. Kalia]
通讯作者: P. Rajak;A. Nakano;P. Vashishta;R. Kalia
EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics
EZFF:用于分子动力学原子间力场的多目标参数化和不确定性量化的 Python 库
DOI: 10.1016/j.softx.2021.100663
发表时间: 2021
期刊: SoftwareX
影响因子: 3.4
作者: [Krishnamoorthy, Aravind, Mishra, Ankit, Kamal, Deepak, Hong, Sungwook, Nomura, Ken-ichi, Tiwari, Subodh, Nakano, Aiichiro, Kalia, Rajiv, Ramprasad, Rampi, Vashishta, Priya]
通讯作者: Vashishta, Priya
FMRG: Artificial Intelligence Driven Cybermanufacturing of Quantum Material Architectures
  • 批准号:
    2240407
  • 项目类别:
    Standard Grant
  • 资助金额:
    $375.0万
  • 财政年份:
    2022
  • 负责人:
    Radhika Nagpal
  • 依托单位:
Collective Robotics for Life Scientists
  • 批准号:
    1353236
  • 项目类别:
    Standard Grant
  • 资助金额:
    $6.38万
  • 财政年份:
    2014
  • 负责人:
    Radhika Nagpal
  • 依托单位:
EMT/BSSE Programmable Self-Adaptation: A Bio-inspired Approach To Multi-agent Robotic Systems
  • 批准号:
    0829745
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2008
  • 负责人:
    Radhika Nagpal
  • 依托单位:
CAREER: Self-Organizing Systems: Engineering and Understanding Robust Collective Behavior
  • 批准号:
    0643898
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2007
  • 负责人:
    Radhika Nagpal
  • 依托单位:
海外基金